人工智能源头治理：基于技术内生性风险的体系建构

Source Governance of Artificial Intelligence: System Construction Based on Endogenous Technological Risks

摘要

AI技术发展在为社会带来巨大便利的同时，也引发了算法偏见、数据污染、模型不可解释性等源头性风险和治理难题。本文基于源头治理视角，剖析技术内生性风险，针对现行AI治理架构的局限性与源头治理的缺位，提出构建技术手段与管理机制并重的源头治理体系的设想，并特别强调：应确立以研发人员为核心的终极问责体系，通过细化个人责任边界、构建资质与追责衔接的管控机制、完善配套法律与追偿制度以及压实全流程伦理履职义务，实现风险与责任的精准匹配。

Abstract

While the development of AI technology brings tremendous convenience to society, it also triggers source-level risks and governance challenges, such as algorithmic bias, data pollution, and model unexplainability. From the perspective of source governance, this paper analyzes the endogenous risks of the technology. In response to the limitations of the current AI governance framework and the absence of source governance, it proposes the establishment of a source governance system that places equal emphasis on both technical means and management mechanisms. Furthermore, it specifically emphasizes the need to establish an ultimate accountability system centered on R&D personnel. This involves defining the boundaries of individual liability, building a control mechanism that links professional qualifications with accountability, improving supporting legal and compensation systems, and enforcing ethical performance obligations throughout the entire process, thereby achieving a precise alignment between risks and responsibilities.

关键词

人工智能 / 源头治理 / 内生性风险 / 技术规制 / 研发人员责任 / 伦理审查

Key words

Artificial intelligence / source governance / endogenous risks / technological regulation / responsibilities of R & D personnel / ethical review

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